A hands-on workout in Hadoop, MapReduce and the art of thinking “parallel”
All Levels, – 13.5 hours, 72 lectures
Average rating 4.2/5 (4.2 (112 ratings) Instead of using a simple lifetime average, Udemy calculates a course’s star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.)
Course requirements:
You’ll need an IDE where you can write Java code or open the source code that’s shared. IntelliJ and Eclipse are both great options.
You’ll need some background in Object-Oriented Programming, preferably in Java. All the source code is in Java and we dive right in without going into Objects, Classes etc
A bit of exposure to Linux/Unix shells would be helpful, but it won’t be a blocker
Course description:
Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data.
This course is a zoom-in, zoom-out, hands-on workout involving Hadoop, MapReduce and the art of thinking parallel.
Let’s parse that.
Zoom-in, Zoom-Out: This course is both broad and deep. It covers the individual components of Hadoop in great detail, and also gives you a higher level picture of how they interact with each other.
Hands-on workout involving Hadoop, MapReduce : This course will get you hands-on with Hadoop very early on. You’ll learn how to set up your own cluster using both VMs and the Cloud. All the major features of MapReduce are covered – including advanced topics like Total Sort and Secondary Sort.
The art of thinking parallel: MapReduce completely changed the way people thought about processing Big Data. Breaking down any problem into parallelizable units is an art. The examples in this course will train you to “think parallel”.
What’s Covered:
Lot’s of cool stuff ..
Using MapReduce to
Recommend friends in a Social Networking site: Generate Top 10 friend recommendations using a Collaborative filtering algorithm.
Build an Inverted Index for Search Engines: Use MapReduce to parallelize the humongous task of building an inverted index for a search engine.
Generate Bigrams from text: Generate bigrams and compute their frequency distribution in a corpus of text.
Build your Hadoop cluster:
Install Hadoop in Standalone, Pseudo-Distributed and Fully Distributed modes
Set up a hadoop cluster using Linux VMs.
Set up a cloud Hadoop cluster on AWS with Cloudera Manager.
Understand HDFS, MapReduce and YARN and their interaction
Customize your MapReduce Jobs:
Chain multiple MR jobs together
Write your own Customized Partitioner
Total Sort : Globally sort a large amount of data by sampling input files
Secondary sorting
Unit tests with MR Unit
Integrate with Python using the Hadoop Streaming API
.. and of course all the basics:
MapReduce : Mapper, Reducer, Sort/Merge, Partitioning, Shuffle and Sort
HDFS & YARN: Namenode, Datanode, Resource manager, Node manager, the anatomy of a MapReduce application, YARN Scheduling, Configuring HDFS and YARN to performance tune your cluster.
Mail us about anything – anything! – and we will always reply 
Full details
Develop advanced MapReduce applications to process BigData
Master the art of “thinking parallel” – how to break up a task into Map/Reduce transformations
Self-sufficiently set up their own mini-Hadoop cluster whether it’s a single node, a physical cluster or in the cloud.
Use Hadoop + MapReduce to solve a wide variety of problems : from NLP to Inverted Indices to Recommendations
Understand HDFS, MapReduce and YARN and how they interact with each other
Understand the basics of performance tuning and managing your own cluster
Full details
Yep! Analysts who want to leverage the power of HDFS where traditional databases don’t cut it anymore
Yep! Engineers who want to develop complex distributed computing applications to process lot’s of data
Yep! Data Scientists who want to add MapReduce to their bag of tricks for processing data
Reviews:
“Nice tutorial with good explanation with visuals and examples.” (Sarath Chandu)
“Course is very well-structured and quite good for a beginner in Hadoop. I haven’t come across an online Hadoop course that has explanations so clear and concise. I am giving 4 stars instead of 5 because the team takes time to respond to queries and that may not be good for a beginner especially when stuck at some step during an installation or the execution of a job. Also, the instructors use Mac and if you are a windows user, you might need some additional steps or google searches to accomplish the tasks that are part of the course.” (Teena)
“too many skips, i understand it all basics, but while installation when your cmd fail you should not say it working and then later she is fixing it and moving so fast, it happen many times, i know you need to read from the document but be prepare so that learner understand whats going on. i really need to google and do all the fixes. its not about you how you fix and resolve the issue for yourself it about how to make it understand to the people who are taking this course. i just did until 1st installation , will feed back once i complete it.” (Akki_2004)
About Instructor:
Loony Corn
Loonycorn is us, Janani Ravi, Vitthal Srinivasan, Swetha Kolalapudi and Navdeep Singh. Between the four of us, we have studied at Stanford, IIM Ahmedabad, the IITs and have spent years (decades, actually) working in tech, in the Bay Area, New York, Singapore and Bangalore.
Janani: 7 years at Google (New York, Singapore); Studied at Stanford; also worked at Flipkart and Microsoft
Vitthal: Also Google (Singapore) and studied at Stanford; Flipkart, Credit Suisse and INSEAD too
Swetha: Early Flipkart employee, IIM Ahmedabad and IIT Madras alum
Navdeep: longtime Flipkart employee too, and IIT Guwahati alum
We think we might have hit upon a neat way of teaching complicated tech courses in a funny, practical, engaging way, which is why we are so excited to be here on Udemy!
We hope you will try our offerings, and think you’ll like them
Instructor Other Courses:
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